Simon Günter
Impact in
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- Handwritten Text Recognition Techniques
- Face and Expression Recognition
- Graph Theory and Algorithms
- Image Retrieval and Classification Techniques
- Artificial Intelligence top 5%
- Neural Networks and Applications
- Stochastic Gradient Optimization Techniques
Papers in
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- Text and Document Classification Technologies 4
- Neural Networks and Applications 2
- Advanced Clustering Algorithms Research 2
-
- Handwritten Text Recognition Techniques 9
- Image Retrieval and Classification Techniques 5
- Face and Expression Recognition 3
- Co-authors
- Horst Bunke (12 shared papers)Nicol N. Schraudolph (4 shared papers)Jin Yu (1 shared paper)S. V. N. Vishwanathan (3 shared papers)Brian J. Parker (1 shared paper)Justin Bedő (1 shared paper)Jin Yu (2 shared papers)
- Journals
- Pattern Recognition Letters (3 papers)Journal of Machine Learning Research (2 papers)BMC Bioinformatics (1 paper)Computing (1 paper)Pattern Recognition (1 paper)
- Partner nations
- SwitzerlandAustraliaDominican Republic
In The Last Decade
Simon Günter
18 papers receiving 649 citations
Peers
Comparison fields: 5 of 108
- Computer Vision and Pattern Recognition 309
- Artificial Intelligence 386
- Signal Processing 114
- Numerical Analysis 39
- Computational Mathematics 4
Countries citing papers authored by Simon Günter
This map shows the geographic impact of Simon Günter's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Simon Günter with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Simon Günter more than expected).
Fields of papers citing papers by Simon Günter
This network shows the impact of papers produced by Simon Günter. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Simon Günter. The network helps show where Simon Günter may publish in the future.
Co-authors
The 7 scholars most cited alongside Simon Günter, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | A stochastic quasi-Newton method for online convex optimization | 2007 | 155 |
| 2 | 2003 | 89 | |
| 3 | 2007 | 75 | |
| 4 | 2002 | 66 | |
| 5 | 2007 | 55 | |
| 6 | 2010 | 51 | |
| 7 | 2004 | 45 | |
| 8 | 2004 | 39 | |
| 9 | 2004 | 34 | |
| 10 | 2003 | 29 | |
| 11 | 2001 | 28 | |
| 12 | 2008 | 15 | |
| 13 | 2003 | 11 | |
| 14 | 2004 | 8 | |
| 15 | Multiple Classifier Systems in Offline Cursive Handwriting Recognition | 2004 | 4 |
| 16 | 2004 | 3 | |
| 17 | 2004 | 3 | |
| 18 | 2004 | 2 |
About Simon Günter
Simon Günter is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Mechanics, Signal Processing and Numerical Analysis, having authored 18 papers that have together received 712 indexed citations. Recurring topics across this work include Handwritten Text Recognition Techniques (9 papers), Image Retrieval and Classification Techniques (5 papers), Text and Document Classification Technologies (4 papers), Sparse and Compressive Sensing Techniques (4 papers), Data Management and Algorithms (3 papers), Face and Expression Recognition (3 papers), Neural Networks and Applications (2 papers) and Advanced Clustering Algorithms Research (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (309 citations), Artificial Intelligence (386 citations), Signal Processing (114 citations), Numerical Analysis (39 citations) and Computational Mathematics (4 citations). Simon Günter has collaborated with scholars based in Switzerland, Australia and Dominican Republic. Frequent co-authors include Horst Bunke, Nicol N. Schraudolph, Jin Yu, S. V. N. Vishwanathan, Brian J. Parker, Justin Bedő and Jin Yu. Their work appears in journals such as Pattern Recognition Letters, Journal of Machine Learning Research, BMC Bioinformatics, Computing and Pattern Recognition.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.